{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoregressive Moving Average (ARMA): Artificial data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import statsmodels.api as sm\n", "import pandas as pd\n", "from statsmodels.tsa.arima_process import arma_generate_sample\n", "np.random.seed(12345)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate some data from an ARMA process:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "arparams = np.array([.75, -.25])\n", "maparams = np.array([.65, .35])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "arparams = np.r_[1, -arparams]\n", "maparams = np.r_[1, maparams]\n", "nobs = 250\n", "y = arma_generate_sample(arparams, maparams, nobs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Now, optionally, we can add some dates information. For this example, we'll use a pandas time series." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/travis/build/statsmodels/statsmodels/statsmodels/tsa/base/tsa_model.py:162: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n", " % freq, ValueWarning)\n" ] } ], "source": [ "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n", "y = pd.Series(y, index=dates)\n", "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n", "arma_res = arma_mod.fit(trend='nc', disp=-1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ARMA Model Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 250\n", "Model: ARMA(2, 2) Log Likelihood -353.445\n", "Method: css-mle S.D. of innovations 0.990\n", "Date: Tue, 17 Dec 2019 AIC 716.891\n", "Time: 23:40:00 BIC 734.498\n", "Sample: 01-31-1980 HQIC 723.977\n", " - 10-31-2000 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1.y 0.7904 0.134 5.878 0.000 0.527 1.054\n", "ar.L2.y -0.2314 0.113 -2.044 0.041 -0.453 -0.009\n", "ma.L1.y 0.7007 0.127 5.525 0.000 0.452 0.949\n", "ma.L2.y 0.4061 0.095 4.291 0.000 0.221 0.592\n", " Roots \n", "=============================================================================\n", " Real Imaginary Modulus Frequency\n", "-----------------------------------------------------------------------------\n", "AR.1 1.7079 -1.1851j 2.0788 -0.0965\n", "AR.2 1.7079 +1.1851j 2.0788 0.0965\n", "MA.1 -0.8628 -1.3108j 1.5693 -0.3427\n", "MA.2 -0.8628 +1.3108j 1.5693 0.3427\n", "-----------------------------------------------------------------------------\n" ] } ], "source": [ "print(arma_res.summary())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2000-06-30 0.173211\n", "2000-07-31 -0.048325\n", "2000-08-31 -0.415804\n", "2000-09-30 0.338725\n", "2000-10-31 0.360838\n", "dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.tail()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(figsize=(10,8))\n", "fig = arma_res.plot_predict(start='1999-06-30', end='2001-05-31', ax=ax)\n", "legend = ax.legend(loc='upper left')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 1 }